Historical image annotation by exploring the tag relevance

Zhang, J; Wu, Q; Xu, J; Lu, J; Phua, R; Curr, K; Tang, Z

Historical image annotation by exploring the tag relevance

Zhang, J

Wu, Q

Xu, J Lu, J Phua, R Curr, K Tang, Z

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Publication Type:: Conference Proceeding
Citation:: Proceedings - 4th Asian Conference on Pattern Recognition, ACPR 2017, 2018, pp. 646 - 651
Issue Date:: 2018-12-13

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Field	Value	Language
dc.contributor.author	Zhang, J https://orcid.org/0000-0002-0033-0494	en_US
dc.contributor.author	Wu, Q https://orcid.org/0000-0001-5641-2483	en_US
dc.contributor.author	Xu, J	en_US
dc.contributor.author	Lu, J	en_US
dc.contributor.author	Phua, R	en_US
dc.contributor.author	Curr, K	en_US
dc.contributor.author	Tang, Z	en_US
dc.date.issued	2018-12-13	en_US
dc.identifier.citation	Proceedings - 4th Asian Conference on Pattern Recognition, ACPR 2017, 2018, pp. 646 - 651	en_US
dc.identifier.isbn	9781538633540	en_US
dc.identifier.uri	http://hdl.handle.net/10453/131603
dc.description.abstract	© 2017 IEEE. Historical images usually contain enormous historical research value and are highly related to the history objects, events and background stories etc. Therefore, annotating these images always requires selecting tags within a large set. In this paper, we propose to annotate historical images by exploring the tag relevance. We measure the tag relevance from three different perspectives, including its visual relevance, its dependencies with other tags and its relationship with location based meta-data. By using tag relevance as guidance, we generate three tag sub-sets and use them to fulfill the annotation. Experimental results on the benchmark dataset indicate the significance of exploring the tag relevance by comparing with the baseline experiments.	en_US
dc.relation.ispartof	Proceedings - 4th Asian Conference on Pattern Recognition, ACPR 2017	en_US
dc.relation.isbasedon	10.1109/ACPR.2017.144	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Historical image annotation by exploring the tag relevance	en_US
dc.type	Conference Proceeding
utslib.for	0802 Computation Theory and Mathematics	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	closed_access	*
pubs.publication-status	Published	en_US

Abstract:

© 2017 IEEE. Historical images usually contain enormous historical research value and are highly related to the history objects, events and background stories etc. Therefore, annotating these images always requires selecting tags within a large set. In this paper, we propose to annotate historical images by exploring the tag relevance. We measure the tag relevance from three different perspectives, including its visual relevance, its dependencies with other tags and its relationship with location based meta-data. By using tag relevance as guidance, we generate three tag sub-sets and use them to fulfill the annotation. Experimental results on the benchmark dataset indicate the significance of exploring the tag relevance by comparing with the baseline experiments.

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http://hdl.handle.net/10453/131603